Results 211 to 220 of about 222,299 (265)

How stochastic cell fate and endoreduplication yield non-random epidermal patterns. [PDF]

Quant Plant Biol
Trozzi N, Majda M.
europepmc   +1 more source

Spatial continuity of neurons explains non-random network architecture. [PDF]

iScience
Reimann MW, Egas Santander D, Kanari L, Barros-Zulaica N.   +3 more
europepmc   +1 more source

Rumor and counter-rumor dynamics in a stochastic delay-fractional framework: a GL-NSFD approach. [PDF]

Sci Rep
Raza A, Lampart M, Shafique U, Baleanu D, Rezk EG, Fadhal E.   +5 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Decomposition methods in stochastic programming

Mathematical Programming, 1997
Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems..
Andrzej Ruszczynski, Ruszczynski Andrzej   +1 more
exaly   +4 more sources

A Stochastic Approximation Method

IEEE Transactions on Systems, Man, and Cybernetics, 1971
A new algorithm for stochastic approximation has been proposed, along with the assumptions and conditions necessary for convergence. It has been proved by two different methods that the algorithm converges to the sought value in the mean-square sense and with probability one.
Naresh K. Sinha, Michael P. Griscik
openaire   +1 more source

Comments on "A Stochastic Approximation Method"

IEEE Transactions on Systems, Man, and Cybernetics, 1972
The results stated in the above paper1 concerning an approved stochastic approximation method are considered. Formulas for the variances of the estimates are derived, and it is found that, in fact, the new algorithm is inferior to previously suggested ones.
Michael A. Budin, Naresh K. Sinha
openaire   +2 more sources

Stochastic Methods

2020
It is clear from the previous chapters of this book that both fault-injection techniques and analytical approaches for cross-layer reliability analysis have both positive and negative aspects that must be carefully analyzed whenever choosing the best approach to evaluate the reliability of a computing system, and none of them alone represents an ...
Alessandro Savino, Alessandro Vallero, Stefano Di Carlo   +2 more
openaire   +3 more sources

Combining the Stochastic Counterpart and Stochastic Approximation Methods

Discrete Event Dynamic Systems, 1997
Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) be the expected performance of a discrete event system (DES), where \(L\) is the sample performance driven by an input vector \(Y\) with a probability density function \(f(y, v)\) and \(\theta\) is a parameter of the sample performance.
Jean-Pierre Dussault, Donald Labrecque, Pierre L'Ecuyer, Reuven Y. Rubinstein   +3 more
openaire   +2 more sources